GarmentPainter: Efficient 3D Garment Texture Synthesis with Character-Guided Diffusion Model

• URL: http://arxiv.org/abs/2603.08228v1
• Date: Mon, 09 Mar 2026 11:01:22 GMT
• Title: GarmentPainter: Efficient 3D Garment Texture Synthesis with Character-Guided Diffusion Model
• Abstract summary: GarmentPainter is a framework for synthesizing high-quality, 3D-aware garment textures in UV space.<n>Our method leverages a UV position map as the 3D structural guidance, ensuring texture consistency across the garment surface.
• Score: 15.770752653490733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating high-fidelity, 3D-consistent garment textures remains a challenging problem due to the inherent complexities of garment structures and the stringent requirement for detailed, globally consistent texture synthesis. Existing approaches either rely on 2D-based diffusion models, which inherently struggle with 3D consistency, require expensive multi-step optimization or depend on strict spatial alignment between 2D reference images and 3D meshes, which limits their flexibility and scalability. In this work, we introduce GarmentPainter, a simple yet efficient framework for synthesizing high-quality, 3D-aware garment textures in UV space. Our method leverages a UV position map as the 3D structural guidance, ensuring texture consistency across the garment surface during texture generation. To enhance control and adaptability, we introduce a type selection module, enabling fine-grained texture generation for specific garment components based on a character reference image, without requiring alignment between the reference image and the 3D mesh. GarmentPainter efficiently integrates all guidance signals into the input of a diffusion model in a spatially aligned manner, without modifying the underlying UNet architecture. Extensive experiments demonstrate that GarmentPainter achieves state-of-the-art performance in terms of visual fidelity, 3D consistency, and computational efficiency, outperforming existing methods in both qualitative and quantitative evaluations.

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